Introduction
Objective: To Monitor changes in 1) neutralising antibody levels 2) rate of waning 3) days to seronegative status.
How do Antibodies Work?
vembedr::embed_youtube(id = "qCRwuxDpthY",
allowfullscreen = T) %>%
use_align("center")Dr. Pamela Bjorkman
vembedr::embed_youtube(id = "Sp1aVTj7IvI",
allowfullscreen = T) %>%
use_align("center")Results
sero_raw <- readxl::read_excel("~/Downloads/Santanu_COVID_events_research.xlsx",
sheet = "long") %>%
clean_names() %>%
mutate(date = ymd(date))
sero <- sero_raw %>%
rename(start = date,
content = event) %>%
mutate(sero = parse_number(outcome)) %>%
mutate(content = str_remove(content, "SARS-CoV-2 ANTI-SPIKE IgG ANTIBODIES")) %>%
mutate(content = str_remove(content, "\\:")) %>%
mutate(content = str_remove(content, "\\*"))Timeline
sero %>%
timevis()IgG Antibody levels over time
fig_igg <- sero %>%
filter(!is.na(sero)) %>%
ggplot(aes(start, sero)) +
geom_line() +
geom_point(aes(size = sero)) +
scale_x_date(labels = scales::date_format("%Y %b")) +
labs(title = "Trend in IgG antibody levels",
x = "",
y = "IgG Antibodies (in AU/ml)",
size = "") +
theme(legend.position = "top")
ggplotly(fig_igg)